class_docstr: Class for Euclidean metrics.
  
  As a Riemannian metric, the Euclidean metric is:
  
  - flat: the inner-product is independent of the base point;
  - positive definite: it has signature (dimension, 0, 0),
    where dimension is the dimension of the Euclidean space.
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docstr: Compute norm of a vector.
  
  Norm of a vector associated to the inner product
  at the tangent space at a base point.
  
  Note: This only works for positive-definite
  Riemannian metrics and inner products.
  
  Parameters
  ----------
  vector : array-like, shape=[..., dim]
      Vector.
  base_point : array-like, shape=[..., dim]
      Base point.
      Optional, default: None.
  
  Returns
  -------
  norm : array-like, shape=[...,]
      Norm.
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code: def norm(self, vector, base_point=None):
      """Compute norm of a vector.
  
      Norm of a vector associated to the inner product
      at the tangent space at a base point.
  
      Note: This only works for positive-definite
      Riemannian metrics and inner products.
  
      Parameters
      ----------
      vector : array-like, shape=[..., dim]
          Vector.
      base_point : array-like, shape=[..., dim]
          Base point.
          Optional, default: None.
  
      Returns
      -------
      norm : array-like, shape=[...,]
          Norm.
      """
      norm = gs.linalg.norm(vector, axis=-1)
      return repeat_out(
          self._space.point_ndim, norm, vector, base_point
      )
  
file_path: geomstats/geomstats/geometry/euclidean.py
imports: import geomstats.backend
  from geomstats.geometry.base import VectorSpace
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 3 for calling the EuclideanMetric obj's underlying member method code with example usage: obj.norm(vector, base_point) and returns: repeat_out